class_docstr: Logistic Regression classifier.
  Parameters:
  -----------
  learning_rate: float
      The step length that will be taken when following the negative gradient during
      training.
  gradient_descent: boolean
      True or false depending if gradient descent should be used when training. If
      false then we use batch optimization by least squares.
code: def __init__(self, learning_rate=0.1, gradient_descent=True):
      self.param = None
      self.learning_rate = learning_rate
      self.gradient_descent = gradient_descent
      self.sigmoid = Sigmoid()
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/logistic_regression.py
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import make_diagonal, Plot
  from mlfromscratch.deep_learning.activation_functions import Sigmoid
label_desc: Use image node_id 1 to create a new LogisticRegression object  with example: obj = LogisticRegression(learning_rate, gradient_descent)